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Updated: Jan 10, 2026

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
Chat, Gemini and Claude at the dinner table: assessing general-purpose AI tools for carbohydrate counting in the
Sandra Goncalves1, Céline Coelho1, Lysiane Pretre1
1Department of Endocrinology and Diabetes, Hôpital Sud-Francilien, 40 Avenue Serge Dassault, 91100 Corbeil-Essonnes, France.
Aims:
Carbohydrate counting is a daily challenge for individuals with type 1 diabetes (T1D). While dedicated apps exist, some patients now use general-purpose AI tools, though their accuracy is uncertain. We evaluated the performance of ChatGPT, Gemini, and Claude compared with expert dietitians.
Methods:
Thirty meals of varying type and complexity were assessed in person by six trained dietitians and photographed for AI-based estimation using a standardized prompt. The primary outcome was absolute error versus reference carbohydrate content; the secondary outcome was the proportion of large overestimations (≥+20 g), which may lead to insulin overdosing. Linear and logistic mixed-effects regression models were applied, with meal as a random effect.
Results:
Mean absolute error was 13 ± 10 g for dietitians, 20 ± 18 g for ChatGPT, 28 ± 26 g for Gemini, and 23 ± 21 g for Claude. Errors were significantly higher for Gemini and Claude, but not for ChatGPT. Large overestimations occurred in 3 % of expert estimates, versus 13 % (ChatGPT), 17 % (Claude), and 38 % (Gemini). Performance did not vary by meal type or complexity.
Conclusions:
General-purpose AI tools can provide carbohydrate estimates but show variable accuracy. They may complement, not replace, structured education in T1D management.
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